Construction Conceptual Cost Estimates Using Support Vector Machine

Abstract

Conceptual cost estimate plays an essential role in project feasibility study. In practice, it is performed based on estimator's experience. However, due to the inaccuracy of cost estimate, budgeting and cost control are planned and executed inefficiently. Support Vector Machines (SVMs), an Artificial Intelligent technique, is used to conduct the construction cost estimate. The algorithms of SVMs solve a convex optimization problem in a relative short time with satisfied accurate solution. Applying SVMs, the construction conceptual cost estimate model is developed for owners and planners to predict the construction cost of a project. The impact factors of cost estimate are identified through literature review and interview with experts. The cost data of 29 construction projects are used as training cases. Based on the training results, the average prediction error is less than 10% and the computation time is less than 5 minutes. The error is satisfied for the conceptual cost estimate of a project during the planning and conceptual design phase. Case studies show SVMs can efficiently and accurately assist planners to predict the construction cost. cost based on their experiences. Nevertheless, building cost is effected by numerous factors. Some of these factors are full of uncertainty such as geological property and decorative class. Due to such complex and uncertain evaluation process, estimators evaluate building cost using a simple linear manner cannot accurately evaluate the costs. As a result, present building cost estimates are rough. Hsieh(2002) employs the Evolutionary Fuzzy Neural Inference Model (EFNIM) to develop an evolutionary construction conceptual cost estimate model. In the model, Genetic Algorithms are primarily used for optimization; Fuzzy Logic for representing uncertainty and approximate reasoning; and Neural Networks for fuzzy input-output mapping. However the computation run time to search optimal solution takes very long. In order to reduce run time, this study using Support Vector Machine (SVM) to estimate construction cost. The remainder of the paper is organized as follows: In section 2, we introduce Neural Networks (NNs) and Evolutionary Fuzzy Neural Inference Model (EFNIM). In section 3, We define the regression problem and present our approach using SVMs. In section 4 this study compares prediction accuracy and required effort of the SVMs with EFNIM and NNs. Finally in section 5, we conclude and discuss avenues for future work.